Discussion and review on evolving data streams and concept drift adapting

Khamassi, Imen; Sayed‐Mouchaweh, Moamar; Hammami, Moez; Ghédira, Khaled

doi:10.1007/s12530-016-9168-2

Cited by 173 publications

(95 citation statements)

References 73 publications

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“…On the other hand, severity can be regarded as the amount of changes that a new concept causes; therefore, a measure of severity can be computed as the percentage of the input space whose target class has changed after the drift. It is worth mentioning that many efforts have been devoted to the characterization of concept drift (Webb, Hyde, Cao, Nguyen & Petitjean, 2016;Khamassi, Sayed-Mouchaweh, Hammami & Ghédira, 2018), however, it is still an open issue in the state of the art due to the complexity of characterizing manifold types of data changes over time.…”

Section: Concept Driftmentioning

confidence: 99%

Spiking Neural Networks and online learning: An overview and perspectives

et al. 2020

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Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts.

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Section: Concept Driftmentioning

confidence: 99%

Spiking Neural Networks and online learning: An overview and perspectives

et al. 2020

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“…Regarding the SL topic, many researches have focused on it due to its mentioned relevance, such as [15][16][17][18][19], and more recently in [20][21][22][23]. The application of regression techniques to SL has been recently addressed in [24], where the authors cover the most important online regression methods.…”

Section: Related Workmentioning

confidence: 99%

“…As it has been previously mentioned, streams of data may evolve over time and may show a change in their data distribution, what provokes that learning algorithms become obsolete. By detecting these drifts we are able to suitably update our algorithms to the new data distribution [16]. ADWIN is a popular two-time sliding window-based drift detection algorithm which does not require users to define the size of the compared windows in advance; it only needs to specify the total size n of a "sufficiently large' window w.…”

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confidence: 99%

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

et al. 2020

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The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

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“…A recent discussion and review of state-of-the-art work regarding learning in non-stationary environments and concept drift has been given in [15]. Khamassi et al group drift-adapting learners into single learners and ensemble learners.…”

Section: Related Workmentioning

confidence: 99%

Supporting sensor orchestration in non-stationary environments

Holst

Lohweg

2018

Proceedings of the 15th ACM International Conference on Computing Frontiers

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The aim of sensor orchestration is to design and organise multisensor systems both to reduce manual design efforts and to facilitate complex sensor systems. A sensor orchestration is required to adapt to non-stationary environments, even if it is applied in streaming data scenarios where labelled data are scarce or not available. Without labels in dynamic environments, it is challenging to determine not only the accuracy of a classifier but also its reliability. This contribution proposes monitoring algorithms intended to support sensor orchestration in classification tasks in non-stationary environments. Proposed measures regard the relevance of features, the separability of classes, and the classifier's reliability. The proposed monitoring algorithms are evaluated regarding their applicability in the scope of a publicly available and synthetically created collection of datasets. It is shown that the approach (i) is able to distinguish relevant from irrelevant features, (ii) measures class separability as class representations drift through feature space, and (iii) marks a classifier as unreliable if errors in the drift-adaptation occur.

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Discussion and review on evolving data streams and concept drift adapting

Cited by 173 publications

References 73 publications

Spiking Neural Networks and online learning: An overview and perspectives

Spiking Neural Networks and online learning: An overview and perspectives

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Supporting sensor orchestration in non-stationary environments

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